Cassie Shum discusses why knowledge graphs serve as a critical foundation for agentic systems. Moving beyond basic RAG, she explains 4 practical architectural patterns: context bundling, decision provenance, code as truth, and agent visibility. She demonstrates an engineering harness built on a knowledge graph to streamline feedback loops, optimize token usage, and maintain system reliability.
Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.
ABSTRACT
Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.
J Thorac Dis. 2026 Aug 31;18(8):950. doi: 10.21037/jtd-2026-1704. Epub 2026 Aug 28.
ABSTRACT
BACKGROUND AND OBJECTIVE: Traditionally, two primary systems have been employed for staging small cell lung cancer (SCLC): the Veterans Administration Lung Study Group (VALG) system and the International Association for the Study of Lung Cancer (IASLC) tumor, node, metastasis (TNM) system. The term "limited disease" is defined differently: VALG characterizes it as disease encompassed within a single tolerable radiation field, while IASLC defines it as the lack of distant metastases (M0). Patients with N3 disease frequently satisfy VALG extensive-stage (ES) criteria while meeting IASLC limited-stage (LS) criteria, resulting in a notable staging discrepancy. Therefore, this review aims to clarify the clinical challenges posed by this staging overlap and provide insights for standardizing staging terminology and optimizing therapeutic decision-making in N3 SCLC.
METHODS: A narrative review utilizing a systematized search strategy was conducted. While strict adherence to PRISMA guidelines was not pursued because the extensive heterogeneity of the literature precluded a formal meta-analysis, rigorous search criteria were applied to minimize selection bias. Databases including PubMed, Web of Science, Embase, the Cochrane Library, and China National Knowledge Infrastructure (CNKI) were searched for literature from January 2000 to March 2026. Studies examining stage N3 SCLC, spatial metastatic burden, and definitional inconsistencies between the VALG and IASLC staging systems were analyzed to assess their effects on treatment dosimetry, systemic therapy, and survival outcomes.
KEY CONTENT AND FINDINGS: The staging overlap in N3 SCLC leads to heterogeneous clinical management depending on its spatial metastatic burden, and this highly variable cohort can be stratified into distinct prognostic subgroups based on the anatomical distribution (single-region vs. multi-region) of the involved lymph nodes.
CONCLUSIONS: These findings should guide clinical trial design and terminology. Clinical decision-making must transcend historical paradigms and technical constraints. Future strategies must incorporate spatial evaluations of metastatic burden alongside innovative multimodal tools, such as artificial intelligence (AI) and multi-omics, to facilitate tailored therapy for SCLC.
In a large international real-world study of non-small cell lung cancer, a multimodal explainable AI model outperformed established biomarkers for immunotherapy outcome prediction and improved physician decision-making.
The quality of biological samples is a major determinant of analytical reliability and translational relevance in patients with pancreatic ductal adenocarcinoma (PDAC). However, variability in specimen procurement, handling, transport, processing, and storage can substantially affect tissue integrity and the robustness of downstream analyses. This paper, promoted by the Pathology and Basic Science Task Force of the Italian Association for the Study of the Pancreas (AISP), brings together experts in pathology, molecular biology, translational research, medical oncology, and gastroenterology to provide practical recommendations for the collection, handling, and pre-analytical management of biological samples. Draft recommendations were discussed during dedicated working group meetings and approved by consensus among all authors, supported by key literature. The document identifies the biological specimen as the critical link between patient care, pathology, and research, and provides guidance for clinicians and professionals involved in sample procurement and processing. By addressing the requirements of different analytical platforms, including genomics, organoid generation, immunophenotyping, pharmacogenomics, and multiplex/spatial analyses, this paper aims to reduce pre-analytical variability, improve diagnostic accuracy, and enhance the clinical and translational value of molecular investigations in pancreatic cancer. Standardised procedures across centres may facilitate comparable data collection, support multicentre studies, and strengthen collaboration between clinicians, pathologists, and research laboratories.
Background: AI is increasingly being integrated into health care workflows, with growing interest in AI systems for documentation, screening, triage, monitoring, and decision support. Mental health care is particularly complex for AI implementation as clinical care largely depends on therapeutic relationships, contextual factors, empathic communication, and interpretation of subtle nonverbal cues. Although AI may offer opportunities to improve efficiency and access to care, its adoption is likely to depend on clinicians’ trust, ethical acceptability, safety, confidentiality, and clarity around professional responsibility. Objective: This scoping review aims to synthesize current evidence on mental health clinicians’ attitudes, perceptions, and beliefs regarding the use of AI tools within mental health care, including the perceived benefits, risks, acceptable use cases, and conditions considered necessary for implementation. Methods: A scoping review was conducted in accordance with Joanna Briggs Institute (JBI) guidance and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Six databases (CINAHL, Embase, PsycINFO, PubMed, Scopus, Web of Science) were searched on May 5, 2026, for studies published from 2020 onwards. Studies were eligible if they examined clinicians’ attitudes, perceptions, or beliefs regarding AI in mental health care. Studies were screened by 2 reviewers, first by title and abstract, and then by full text. Data extraction included publication year, country, methodology, participant occupation, AI type, outcome domains, and relevant findings. Results: Database searches retrieved 12,356 records. Following screening, 35 records were included. Overall, clinicians demonstrated cautious optimism toward AI, particularly when positioned as supplementing instead of replacing clinical expertise. Perceived benefits centered on reducing administrative burden, supporting documentation, synthesizing large volumes of information, and improving access to care, particularly between sessions. However, clinicians also reported limited AI literacy and prior use, and were concerned about privacy, confidentiality, governance, data ownership, clinical safety, unsafe or inaccurate outputs, overreliance, unclear accountability, professional role boundaries, and impacts on therapeutic relationships. Clinicians emphasized the need for education, clear guidelines and governance, role clarity, co-designed systems, human oversight, and real-world, ongoing evaluation. Conclusions: This review is innovative in shifting focus from the technical performance of AI tools to the perspectives of the clinicians who will be expected to use, interpret, explain, and remain accountable for them in mental health care. Unlike previous reviews, this review provides a clinician-centered understanding of AI adoption, highlighting that acceptability depends not only on what AI can do but also on whether it can be integrated safely, ethically, and in ways that preserve professional judgment and therapeutic relationships. These findings suggest that implementation should begin with lower-risk (eg, administrative), clinician-facing applications, be supported by education and governance, and be evaluated in mental health settings before wider adoption. These insights provide practical direction for responsible, clinician-centered implementation across mental health services.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Meet the under-35s shaping the future of biotech
Every year, MIT Technology Review puts together our 35 Innovators Under 35, a list of some of the brightest and best young minds working across science and technology. This year’s honorees include nine people transforming biotech, whose work spans everything from lifesaving innovations to groundbreaking longevity tech.
Their innovations include a “reprogramming” therapy that reverses vision loss, tiny brain electrodes inspired by Japanese art, and a personalized gene-editing treatment for a baby with a rare genetic disorder. There are even efforts to design new viruses with generative AI, which (hopefully) will produce new drugs or soak up pollution.
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
Biotechnology is one of four categories in our 35 Innovators Under 35 list for 2026, featuring young people worldwide doing groundbreaking work in science and technology. Meet the rest of them here, or explore the full list across the AI, computing and robotics, biotechnology, and climate and energy categories.
This founder is making cheaper, cleaner steel
The steel industry isn’t exactly known for innovation. Very little has changed about purifying iron ore since the process was invented and commercialized in the 1850s. But Laureen Meroueh, founder of Hertha Metals, has an idea that could change that.
Meroueh may have found a way to clean up steelmaking without driving up the price. Her new furnace turns iron ore into refined liquid steel in a single step and swaps coal for natural gas. Together, those changes slash emissions by at least half, she says, and cut costs by 25% compared with steelmaking as usual.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Anthropic says it has blocked potential plots to build biological weapons The company identified five such cases. (NYT $) + And six cases of using AI to build software for conventional weapons (BBC) + Governments are also using Claude for surveillance.(Axios) + While Russia-linked hackers used it to automate attacks on Ukraine. (Quartz) + The threats were revealed in a new Anthropic report. (Guardian) + Bill Gates says AI needs new guardrails. (MIT Technology Review)
2 California has banned addictive social media features for under-16s The law prohibits infinite scroll and autoplay. (Guardian) + It also introduces new rules for AI and companion chatbots. (Reuters $) + It’s the first law of its kind in the US. (NYT $) + Social media encourages the worst AI boosterism. (MIT Technology Review)
3 Two AI researchers have left Anthropic and Google over safety risks They left a day after Jacob Coxon’s viral departure from Anthropic. (NBC News) + Elon Musk called their concerns a “setup” and a “psyop.” (Guardian) + AI fears are pushing Congress toward tougher regulation. (WSJ $)
4 Sam Altman is pitching OpenAI’s cyber defenses to power companies The meetings followed reports of AI attacks on critical systems. (Politico $) + Altman also told staff that OpenAI is open to slowing down AI. Bloomberg $)
5 After years of fighting AI, music labels are starting to embrace it Universal is partnering with ElevenLabs on an AI remix platform.(Gizmodo) + AI is complicating definitions of creativity. (MIT Technology Review)
6 Chinese drugmakers are challenging US dominance in weight-loss drugs They’re developing hundreds of GLP-1 treatments for global markets. (WSJ $)
7 Electric air taxis have begun official test flights in Texas They’re the first flights under the White House’s new pilot program. (Verge)
8 Chinese drones are helping to rescue survivors of Nepal’s floods They’re delivering food and airlifting bodies from flood-hit areas. (Ars Technica)
9 NASA and IBM have built an AI model to map the moon It could help locate ice and identify safer landing sites. (Register)
10 One man is on a quest to digitally preserve America’s public restrooms His Restroom Archive is a museum-style repository of 3D scans. (404 Media)
Quote of the day
“I didn’t ask Facebook to build a profile of my family—I posted a video of me singing in the car with my kids.”
—Kalie Roberts, a travel content creator, says in an Instagram reel that Meta AI used years of Facebook posts to piece together her children’s identities and pinpoint where her family lives.
One more thing
Chinese tech workers are starting to train their AI doubles—and pushing back
In April, a GitHub project called Colleague Skill struck a nerve by claiming to “distill” a worker’s skills and personality—and replicate them with an AI agent. Though the project was a spoof, it prompted a wave of soul-searching among otherwise enthusiastic early adopters.
A number of tech workers told MIT Technology Review that their bosses are already encouraging them to document their workflows for automation via tools like OpenClaw. Many now fear that they are being flattened into code and losing their professional identity.
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+ Worried about Flock cameras? These guys designed a car to fool them. + Webb’s Near-Infrared Camera has captured a galactic merger’s dazzling final phase. + An exquisitely preserved 66-million-year-old bird feather was found in a fossilised dinosaur dropping. + A plucky preservationist travelled 1,700 miles and made 52 calls from a rare phone box to keep it in service.
Every year, MIT Technology Review puts together a list of some of the brightest and best young minds working across science and technology. Our 35 Innovators Under 35 are the ones to watch—people whose research and technical work stands to shape the future of their fields.
This year, the list includes nine people who are transforming biotech. And this week, I’m going to give you a taste of some of the very cool stuff five of them are working on, which includes lifesaving innovations and groundbreaking “age reversal” tech.
1. Preventing maternal deaths
Let’s start with Paschal Kija, a 28-year-old who has developed a device to treat postpartum hemorrhage—a dangerous birth complication that contributes to around 29% of maternal deaths in his home country, Tanzania. The Mkanda Salama (“Safe Wrap” in Swahili) is easy to use and costs just $70. A study found that it stopped postpartum bleeding in 73% of women within 20 minutes.
2. Making brain electrodes inspired by Japanese art
For decades, scientists have been developing, testing, and implanting brain electrodes. These devices are literally inserted into people’s brains, so while they can help us understand brain activity and treat various neurological disorders, it’s not totally surprising that they can also cause a bit of damage. Xiao Yang, 34, is working on ultra-small electrodes, which she hopes will have less of an impact on surrounding brain tissue. Her electrodes are flexible, too—in fact, they look a lot like actual neurons.
Yang is also creating sheets of electrodes to study brain cells in the lab. Inspired by kirigami—the traditional Japanese art of cutting paper to form three-dimensional shapes—she’s created a sheet of electrodes with a honeycombed structure shaped like a spiral basket. And she’s already using it to study brain cells.
3. Developing an all-new treatment for baby KJ
In 2024, Kyle “KJ” Muldoon Jr. was born with a rare and potentially fatal genetic disorder. Sarah Grandinette was a member of a team that developed an entirely new, personalized treatment for him—a gene-editing therapy essentially designed to correct a genetic misspelling.
Grandinette, who is now 26, created cells with KJ’s genetic variant and used them to screen gene-editing approaches; then she tested potential medicines in mice and monkeys. KJ ultimately got his first dose of the resulting treatment when he was about seven months old. He responded well and was eventually discharged from hospital. He’s “doing pretty great,” she says.
4. Reversing the aging process to treat eye disease
The buzziest tech in longevity right now centers on reprogramming—attempts to rewind the age of cells by resetting them to a more embryonic-like state. In a study published in 2020, Yuancheng (Ryan) Lu (now 34) and his colleagues showed that a reprogramming therapy reversed vision loss in aged, blind mice. Now an almost identical version of that therapy is being tested in people with eye disease. Life Biosciences, the company developing the drug, dosed its first volunteer in June.
5. Using AI to design new viruses
Last year, Samuel King used a generative AI model to come up with new genetic blueprints for bacteriophages—teeny viruses that can infect bacteria. Once he had those blueprints, he printed them out as strands of DNA. In experiments, he found that those AI-designed viruses could create new copies of themselves, burst out of bacterial cells, and infect other nearby bacteria. Viruses aren’t alive, but King, 27, hopes that AI-designed life forms might one day be used to make drugs or soak up pollution.
You can read more about these innovators, and the others on the biotech list, here.
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
In the randomized phase 2 ImmunoPRISM trial, patients with high-risk smoldering multiple myeloma (MM) showed higher rates of complete clinical responses in response to treatment with teclistamab compared with lenalidomide–dexamethasone, although longer follow-up is required to determine durable prevention of progression to MM.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
God told them to sell crypto. Their investors lost everything.
When Eli Regalado first heard God speak to him, he wondered whether he was hallucinating. According to Eli and his wife, Kaitlyn, He told them to get married, buy a house, and start having kids. Then in 2021, divine guidance steered them in an unexpected new direction: crypto.
That October, the Regalados later testified in court, they received holdings in a little-known digital coin. “Take this to my people for a wealth transfer,” Eli heard God say. Over time, they came to believe that He wanted them to launch their own coin.
The Regalados created INDXcoin, which they promoted through family, friends, and contacts in evangelical Christian circles. In all, more than 500 people handed over more than $3 million. But within a year, the project collapsed. Investors lost it all, leaving many to wonder where the funds went and whether they had fallen victim to an elaborate fraud.
This article is part of the Big Story series, the home of MIT Technology Review’s most important and ambitious reporting. You can read the rest of the series here.
The story was produced in partnership with Type Investigations and with support from the Fund for Investigative Journalism.
This road map could help us decide whether to deploy solar geoengineering
Scientists have spent half a century exploring whether we could counteract climate change by releasing reflective particles into the stratosphere, mimicking the cooling effects of volcanic eruptions. But even after hundreds of studies, we still don’t know how well it would work or what else it might do—and there’s no systematic plan for clearing up that uncertainty.
Reflective, a research organization, has now attempted to fill that gap. The San Francisco nonprofit has published a detailed road map of the experiments, studies, and infrastructure that it says would be needed to make informed decisions about the use of solar geoengineering, MIT Technology Review can reveal.
This founder is teaching chips how to recycle (their energy)
Throughout the history of the computer chip, engineers have treated waste heat as an inevitable cost of a calculation. Hannah Earley, however, thinks it’s a design choice.
Earley, 31, is cofounder and CTO of Vaire Computing, which builds chips that recycle energy usually thrown away as heat, a strategy known as reversible computing. The approach could make data centers (and our laptops and phones) much more energy efficient.
Last year, Vaire announced a key breakthrough: a chip with a resonator that recovered more energy than it lost, even after the energy needed to power the component was taken into account.
Hannah Earley is one of the computing and robotics honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology, AI, computing and robotics, and climate and energy categories.
Can the US battery market untangle from China?
—Casey Crownhart
The US energy storage market is growing at a record pace, which could shore up the grid and cut emissions. Crucially, this is all happening with the help of cheap Chinese batteries, which the Trump administration is trying to phase out.
Reducing reliance on any single source of crucial energy technology makes sense. But the tension raises a broader question for me: how much should countries take advantage of cheap, available tech, and how much should they cut themselves off from foreign sources to develop their own, even if it costs more?
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI’s agents used at least 10 websites for unauthorized communications Researchers found they bypassed restrictions on posting online.(Reuters $) + The company faces a Senate probe into the Hugging Face breach. (Axios) + Its hacking issues may indicate cultural problems. (MIT Technology Review)
2 Another Anthropic model hacked a real system during testing A misconfigured environment gave it internet access. (CBS News) + The January incident went undetected until last month. (Reuters $) + AI agents are not your “coworkers.” (MIT Technology Review)
3 Apple has entered the foldable phone market with the $1,999 iPhone Duo It opens into a 7.6-inch display and launches October 23. (NPR) + Apple is betting its design and privacy will give it an edge. (Reuters $) + And that foldables can solve the smartphone’s sameness problem. (NPR $) + Samsung responded with a campaign touting its foldable lead. (CNBC) + In China, Apple enters a crowded market dominated by Huawei. (SCMP)
4 US prosecutors have called Huawei a criminal enterprise at trial They accuse the company of stealing American technology. (Reuters $) + And helping Iran snoop on its citizens. (AP News) + The trial could impact Trump’s upcoming meeting with Xi. (WSJ $)
5 California is warming to nuclear power after decades of opposition The state may extend Diablo Canyon and lift its ban on new reactors. (NYT $) + China is betting on big nuclear reactors. (MIT Technology Review)
6 Chinese professionals are becoming gig workers training AI Lawyers and engineers are training models for extra income. (Rest of World) + Gig workers are training humanoids at home. (MIT Technology Review)
7 The new Apple Watch can listen to conversations happening nearby Apple says users must opt in, but others cannot. (Wired $)
8 Pink noise during sleep could help the brain clear away waste Timed bursts boosted brain fluid flow in a small study. (New Scientist $)
9 A lost supercontinent may have triggered the explosion of life Gondwana’s formation fueled volcanic activity and warmed the planet. (404 Media)
10 GTA VI has sparked a debate over whether virtual romance is cheating Players can date, have sex with, and shower gifts on virtual partners. (Guardian)
Quote of the day
“We must work to crush any dissent to Doom’s vision of public safety.”
—A Seattle policy adviser dressed as Doctor Doom protests the city’s expanding network of Flock and Axon surveillance systems at a Public Safety Committee meeting, 404 Media reports.
One more thing
Digging for clues about the North Pole’s past
In the past, getting to the North Pole involved a treacherous trip through ice many meters thick. But last year, a research vessel encountered open water and thin ice, which created an easy passage. It provided a reminder of how quickly the Arctic is changing.
Now scientists are digging deep below the seabed to find out if the Arctic Ocean was ever ice-free—and what that could mean for the future of Earth’s northernmost waters.
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+ Dutch kids have been declared the world’s happiest (again). Here’s why. + Travel through music history by picking a country and decade on Radiooooo. + These 16 majestic aerial photos reveal wildlife from perspectives you rarely see. + A Toronto cafe is pushing croissant engineering to new heights with its egg-shaped, custard-filled “Crogg.”
On July 22, 2026, a transmission line fault in Ashburn, Virginia—the heart of the world’s largest data center cluster—knocked more than 3 gigawatts of load off the grid in seconds. And it wasn’t the first time. Two years earlier, a single failed surge arrester dropped roughly 60 Virginia facilities and 1,500 megawatts at once. No one could anticipate so much uniform load responding to grid faults the same way, at the same time.
The AI power debate is mostly about generation: more turbines, more solar, more transmission. The grid needs more electrons. But the outages in Virginia weren’t supply failures; they were architecture failures. And a giant wave of interconnections is arriving on that same architecture, putting grid reliability at risk. It’s a problem nobody wants to own.
Asking more from the grid
The grid was built around predictable loads: steel mills, refineries, and houses at dinnertime. Different load sizes, same process—drawing power smoothly, misbehaving occasionally, and recovering gracefully.
But AI data centers don’t behave that way.
An AI campus can swing 70% of its load in milliseconds during a training run, then trip offline just as fast at the first sign of trouble upstream to protect billions in compute. Each is rational alone. Together, at gigawatt scale, they’re a problem the grid has never solved—and the next wave of data center campuses is planned at exactly that scale.
Where the old stack breaks
The standard data center power stack hasn’t changed in decades. Medium-voltage power arrives, transformers step it down, low-voltage uninterruptible power supply (UPS) units condition it, and it reaches the racks. Push that design to AI scale, and it cracks in three places.
First, the UPS sits deep inside the building, close to the racks. But its batteries are an undersized spare tire, designed to handle an outage for a few minutes, not to absorb load swings this fast and volatile around the clock.
Second, the UPS spends most of its life in bypass. Legacy converters waste enough power that operators run in eco-mode: A static switch feeds the racks directly from the grid and nothing filters in either direction. The compute’s swings go out raw, and grid transients—sub-millisecond events that can damage or take down equipment—come in too fast for any switch to catch.
This isn’t sloppy engineering. It’s careful engineering the load has outgrown.
Moving into the path
The fix is three moves, made together.
Move it up—from 480 volts to medium voltage (13.8 kilovolts and higher), the voltage large sites draw from the grid.
Move it out—from the data hall to modular enclosures near the substation so the building holds only compute and the cooling that keeps it alive.
Move it into the path—instead of a battery that watches and reacts, a system every electron runs through, all the time. There’s nothing to detect and nothing to switch because nothing was ever routed around it.
On paper, three straightforward upgrades. In practice, they rewrite every line item downstream.
Making the change
When thousands of GPUs spin up together, the system absorbs the swing and hands the grid a flat load profile. When a disturbance hits, the equipment behind it never notices. A difficult neighbor becomes a predictable one. And when the utility needs help, it becomes a useful one.
Interconnection changes, too. The utility certifies one medium-voltage box instead of untangling every transformer, UPS, chiller, pump, and switchgear lineup behind it. Engineers swap chip generations without a fresh interconnection study. Months come off the permitting timeline.
Inside the fence, UPS rooms become compute or cooling space. Density per construction dollar climbs.
And the economics flip. Equipment that runs at medium voltage, sits outside, and stores its own energy can qualify for tax credits, and earn revenue in grid programs like peak shaving and demand response. Backup power stops being insurance and starts paying for itself.
The architecture test
In early 2026, we tested a full-scale system at the National Laboratory of the Rockies, a U.S. Department of Energy facility and the only place in the Western Hemisphere that can replicate real grid faults and AI-scale load swings concurrently in the same loop.
We hit it from both directions: real AI load profiles hit the compute side at full medium voltage. Grid faults hit the utility side, including a full zero-voltage event. The compute side didn’t flinch. Neither did the grid side. It cleared the large-load voltage ride-through requirements from the Electric Reliability Council of Texas (ERCOT), the grid operator, with room to spare.
Those rules exist because operators no longer take facilities this size on faith, and more are coming. Most of the industry treats them as hurdles. A medium-voltage, inline system clears them out of the box. Compliance isn’t an added feature. It’s what the architecture does.
The new layer
Much of what looks like a grid problem in the AI buildout sits inside the fence, in equipment sized for a load that no longer exists. Move the right pieces up, out, and into the path, and a grid liability becomes a grid asset. Density goes up. Permitting time comes down. Backup power earns its keep.
The engineering works—and the next wave of AI factories is being built on it. The industry hasn’t named this layer yet. We call it the medium-voltage AI UPS. The name matters less than the choice: those factories can arrive as a strain on the grid or as strength for it. We already know how to build the second kind.
This content was produced by ON.energy. It was not written by MIT Technology Review’s editorial staff.
The US is hitting records for the rapid growth of its energy storage market. That’ll go a long way to shoring up the grid, increasing reliability and also cutting emissions, since batteries can help store energy from intermittent renewables like wind and solar.
Crucially, this is all happening with the help of cheap Chinese batteries, though there’s been a concerted effort to reduce the US’s reliance on them. Most recently, in an executive order in late August, the Trump administration declared a national emergency that essentially bans Chinese batteries from being used in grid-scale energy storage systems.
There’s an argument to be made about reducing reliance on any single source of a crucial energy technology. But all this tension raises a broader question for me: How much should countries take advantage of cheap, available tech, versus cutting off major sources to force development of their own factories even if that comes at a higher cost?
This is hardly America’s first push to move away from Chinese influence in the battery supply chain. One of the major policy tools used in recent years is restricting the tax credits designed to incentivize use of the new technologies. Limiting the types of projects that are eligible can help reduce the cost of local technologies so they’re more competitive with otherwise cheaper imported options.
Back in 2022, the US government designed the tax credits that were part of the Inflation Reduction Act to restrict where a battery’s minerals could be mined, processed, or recycled, as well as where a battery and its components were assembled.
Those tax credits underwent a makeover in 2025, but the Trump administration has taken a similar tack. New legislation requires that starting in 2026, 55% of the cost of materials used for new energy storage projects must come from outside China and other restricted countries or the projects won’t qualify for tax credits.
And we can’t forget about tariffs. Import taxes for batteries increased to 25% in January, up from 7.5%.
But the new executive order is a more drastic move. It bans the installation of “any foreign-produced bulk-power system electric equipment” that poses a national security risk. The order specifically calls out battery energy storage systems, as well as inverters and transformers.
“An outright ban was a bit of a surprise, and it does create a bit of concern for domestic players in the US,” says Shan Tomouk, energy storage and energy lead for Benchmark Mineral Intelligence, an energy industry analyst.
The move is likely to slow deployment of grid-connected energy storage projects in the near term, according to analysis from BloombergNEF, an energy consultancy. Projects could face delays as developers wait for clarity on the rules.
Depending on the detailed guidance from the Department of Energy, which is expected by the end of the year, some projects may need to find alternative sources for their cells, whether they’re domestically produced or imported from other countries. These will likely be more expensive than Chinese imports, says Isshu Kikuma, an energy storage analyst at BloombergNEF. “Worst case, those projects could get canceled,” he says.
Technically, the order applies even to existing energy storage plants, though it’s unlikely that they’ll be taken offline because of their batteries’ origin. Since most of these plants currently use Chinese batteries, enforcing the order to the letter would essentially mean removing most installed battery energy storage from the US grid, Kikuma says.
In the longer term, the US will eventually be able to meet its own demand for batteries. The country could have enough capacity by about 2030, though some factories may not ramp up or run at their full capability, meaning domestic supply won’t actually meet demand until later in the 2030s.
New factories from LG Energy Solutions, Samsung SDI, Ford, and SK On are set to come online or ramp up by next year. In an ironic twist, a slowing EV market is helping, as some factories originally designed for vehicle batteries are retooling to build cells for grid storage instead.
But it will come at a cost. Today, batteries produced in the US are still significantly more expensive than those made in China. Even switching to imports from other countries like South Korea would likely be more expensive.
This is a crucial issue that goes beyond the US and even beyond batteries. China is miles ahead of much of the rest of the world on technologies like solar panels and batteries. Through years of government support and experience with research and manufacturing, the nation is an energy powerhouse.
There’s a delicate political balance to maintain as the world figures out how to navigate this situation. There’s cheap technology on offer, which can help drastically reduce emissions and energy costs. But there can be risks associated with relying too much on any one player for crucial technologies.
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KRAS is mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), where hotspot alterations in codons 12, 13, and 61 drive tumor initiation and progression. Although distinct biochemical properties have been described for individual KRAS mutants, whether they generate unique allele-specific signaling programs in PDAC cells remains unresolved. Here, we systematically interrogated the molecular consequences of seven common KRAS mutant variants in reconstituted isogenic, KRAS-deficient PDAC cell lines by integrated transcriptomic, proteomic, and phosphoproteomic profiling. We found that baseline cellular state, rather than allele identity, was the predominant driver of molecular variation. Comparisons with established KRAS reference signatures revealed significant but moderate overlap at the mRNA level and less so at the proteome level. Pathway analyses highlighted interferon response and mitochondrial translation-related proteins as recurrently altered across mutant alleles, while phosphoproteomic data confirmed robust ERK1/2 activity and suppression of DYRK kinase substrates by mutant KRAS expression. Importantly, no robust mutant allele-specific molecular programs were identified in our KRAS-reconstituted cell lines. Together, our study establishes a comprehensive multi-omics resource for KRAS signaling in PDAC and demonstrates that cellular context exerts a stronger influence than allele identity in shaping molecular profiles, with implications for interpreting putative allele-specific signaling dependencies.
CRISP, a vision-based pathology foundation model developed exclusively from frozen section slides, supports treatment decision-making throughout the surgical workflow with superior performance to current foundation models and extensive validation, including in a prospective cohort.
A suite of large pre-trained language models accurately extracts computable clinical data from unstructured electronic health records and integrates the findings into knowledge graphs that can facilitate understanding patient trajectories and treatment responses in the real world.
Analysis of circulating tumor-DNA (ctDNA) provides a molecular assessment that can complement routine imaging in childhood cancer management. Detailed monitoring of ctDNA levels may provide information on treatment efficacy and resistance, minimal residual disease and allows for early detection of relapse. Here, tumor-informed ctDNA analysis was applied to 90 blood plasma samples collected from eight children with malignant tumors. Four to ten tumor-specific mutations per patient were assessed using SiMSen-Seq, a digital sequencing approach utilizing unique molecular identifiers (UMIs). The effects of individual SiMSen-Seq assays and plasma samples were evaluated in relation to their impact on background error rate, number of detected target molecules and mutant calling using different UMI family size cutoff settings. The use of at least two sequencing reads per UMI provided the best overall performance by generating the highest number of detected target molecules and hence the optimal chance to detect low-frequent mutations. Data were consistent between SiMSen-Seq assays and plasma samples, providing robust ctDNA profiling over time for all patients. In conclusion, the results show that optimal use of UMIs in tumor-informed ctDNA analysis enables sensitive molecular readout that can assist in management of childhood cancers.
Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.
ABSTRACT
Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.
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